Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud.
Regardless of the size of the data set, Amazon Redshift offers fast query performance using the same SQL-based tools and business intelligence applications that you use today
The Amazon Redshift service manages all of the work of setting up, operating, and scaling a data warehouse.
An Amazon Redshift data warehouse is an enterprise-class relational database query and management system.
Amazon Redshift achieves efficient storage and optimum query performance through a combination of massively parallel processing, columnar data storage, and very efficient, targeted data compression encoding schemes.

Data warehouse system architecture

11. Amazon RedshiftOverviewData warehouse system architecturePerformanceAmazon Redshift best practicesDistribution stylesReference

A cluster is composed of one or more compute nodes.
If a cluster is provisioned with two or more compute nodes, an additional leader node coordinates the compute nodes and handles external communication.
Your client application interacts directly only with the leader node. The compute nodes are transparent to external applications.
A compute node is partitioned into slices. Each slice is allocated a portion of the node's memory and disk space, where it processes a portion of the workload assigned to the node.
The leader node manages distributing data to the slices and apportions the workload for any queries or other database operations to the slices. The slices then work in parallel to complete the operation.
- When you create a table, you can optionally specify one column as the distribution key. When the table is loaded with data, the rows are distributed to the node slices according to the distribution key that is defined for a table.

Massively parallel processing
- Multiple compute nodes handle all query processing leading up to final result aggregation, with each core of each node executing the same compiled query segments on portions of the entire data.
- Amazon Redshift distributes the rows of a table to the compute nodes so that the data can be processed in parallel.
Columnar data storage for database tables drastically reduces the overall disk I/O requirements and is an important factor in optimizing analytic query performance.
Data compression reduces storage requirements, thereby reducing disk I/O, which improves query performance.
- The best way to enable data compression on table columns is by allowing Amazon Redshift to apply optimal compression encodings when you load the table with data
The Amazon Redshift query execution engine incorporates a query optimizer that is MPP-aware and also takes advantage of the columnar-oriented data storage.
To reduce query execution time and improve system performance, Amazon Redshift caches the results of certain types of queries in memory on the leader node.
The leader node distributes fully optimized compiled code across all of the nodes of a cluster.

Choose the best sort key
- If recent data is queried most frequently, specify the timestamp column as the leading column for the sort key.
- If you do frequent range filtering or equality filtering on one column, specify that column as the sort key
- If you frequently join a table, specify the join column as both the sort key and the distribution key.
Choose the best distribution style
- Distribute the fact table and one dimension table on their common columns.
- Choose the largest dimension based on the size of the filtered dataset.
- Choose a column with high cardinality in the filtered result set.
- Change some dimension tables to use ALL distribution.
Let COPY choose compression encodings
- You can specify compression encodings when you create a table, but in most cases, automatic compression produces the best results.
- Automatic compression balances overall performance when choosing compression encodings.
Define primary key and foreign key constraints
Use the smallest possible column size
Use date/time data types for date columns

Use a COPY command to load data
Use a single COPY command to load from multiple files
Split your load data into multiple files
Compress your data files
Verify data files before and after a load
If a COPY command is not an option and you require SQL inserts, use a multi-row insert whenever possible.
Use a bulk insert operation with a SELECT clause for high-performance data insertion
Load your data in sort key order to avoid needing to vacuum.
If you need to add a large quantity of data, load the data in sequential blocks according to sort order to eliminate the need to vacuum.
If your data has a fixed retention period, you can organize your data as a sequence of time-series tables. In such a sequence, each table is identical but contains data for different time ranges.You can easily remove old data simply by running a DROP TABLE command on the corresponding tables.

AUTO ——Amazon Redshift assigns an optimal distribution style based on the size of the table data.
EVEN-——The leader node distributes the rows across the slices in a round-robin fashion, regardless of the values in any particular column.
KEY——The rows are distributed according to the values in one column.
ALL——A copy of the entire table is distributed to every node.

https://docs.aws.amazon.com/redshift/latest/dg/welcome.html

https://docs.aws.amazon.com/redshift/latest/mgmt/welcome.html

https://docs.aws.amazon.com/redshift/latest/gsg/getting-started.html